gendata_Mean

Generate simulated data from for high-dimensional mean model.

A general framework of two directional simultaneous inference
is provided for high-dimensional as well as the fixed dimensional models with manifest
variable or latent variable structure, such as high-dimensional mean models, high-
dimensional sparse regression models, and high-dimensional latent factors models.
It is making the simultaneous inference on a set of parameters from two directions,
one is testing whether the estimated zero parameters indeed are zero and the other is
testing whether there exists zero in the parameter set of non-zero. More details can be
referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.

Wei Liu

TOSI

Two-Directional Simultaneous Inference for High-Dimensional
Models

Huazhen Lin

gendata_Mean function

<dl> <dt>n</dt>
<dd>a positive integer, the sample size.</dd> <dt>p</dt>
<dd>an positive integer, the variable dimension.</dd> <dt>s0</dt>
<dd>a positive integer, the number of nonzero components of mean .</dd> <dt>seed</dt>
<dd>a nonnegative integer, the random seed, default as 1.</dd> <dt>rho</dt>
<dd>a positive number between 0 and 1, controlling the correlation of data.</dd> <dt>tau</dt>
<dd>a positive number, controlling the magnitude of covriance matrix.</dd></dl>

Arguments

Author

Generate simulated data — gendata_Mean

<dl>

 <dt>n</dt>
<dd>a positive integer, the sample size.</dd>

 <dt>p</dt>
<dd>an positive integer, the variable dimension.</dd>

 <dt>s0</dt>
<dd>a positive integer, the number of nonzero components of mean .</dd>

 <dt>seed</dt>
<dd>a nonnegative integer, the random seed, default as 1.</dd>

 <dt>rho</dt>
<dd>a positive number between 0 and 1, controlling the correlation of data.</dd>

 <dt>tau</dt>
<dd>a positive number, controlling the magnitude of covriance matrix.</dd>

</dl>

gendata_Mean: Generate simulated data

Description

Usage

Value

Arguments

Author

Examples